Anomaly Detection COV Structure
Description
The Anomaly Detection Algorithm (ADA) models are intended to solve several key problems for U.S. Customs and Border Protection (CBP) related to the screening of passenger and cargo vehicles. Improving the detection of anomalies and contraband, enhancing efficiency in image review, enhance human capability to consistently detect items of interest or concern, addressing high traffic volumes and resource constraints, and supporting the analysis of complex inspections.
Detailed example
Bounding boxes around an anomaly or unidentifiable object(s) within an image or any portion of the image that cannot be identified or explained.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
CBP is seeking Anomaly Detection Algorithm (ADA) models capable of operating on CBP systems to enable rapid screening of commercially owned vehicles (CoVs). The objective is to develop a suite of algorithms that enhance CBP's Non-Intrusive Inspection (NII) image analysis, improving the detection of anomalies and contraband. These algorithms are intended to assist CBP officers in efficiently reviewing images, with a particular focus on identifying concealed contraband and anomalies in passenger vehicles and cargo conveyances. The implementation of ADA models will enhance human capability to consistently detect items of interest or concern, including concealed objects, in vehicles entering the United States. Additionally, these algorithms will enhance throughput efficiency at ports of entry, enabling the expedited processing of compliant vehicles while maintaining robust security standards.
Audit / financial statement impact
Computer Vision is intended to detect anomalies in non-intrusive inspection images. The images are then shared with officers who review the detections within the images (represented as a polygon). If the officer feels physical review is required, the vehicle is moved to secondary inspection for more thorough review by an officer. The output of the AI does not serve as a principal basis for a decision or action.
Controls / human review
ATO: Not reported; PIA: Not published